url = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv'
read_csv(url)
Parsed with column specification:
cols(
date = col_date(format = ""),
county = col_character(),
state = col_character(),
fips = col_character(),
cases = col_double(),
deaths = col_double()
)
library(dplyr)
library(ggplot2)
library(tidyr)
library(zoo)
covid<-read.csv(url)
head(covid)
state.of.interest ="California"
covid$date = as.Date (covid$date)
covid %>%
filter(state == state.of.interest) %>%
group_by(date) %>%
summarise(cases = sum(cases)) %>%
mutate(newCases = cases - lag(cases),
roll7 = rollmean(newCases, 7, fill = NA, align="right")) %>%
ungroup() %>%
ggplot(aes(x = date)) +
geom_col(aes(y = newCases), col = NA, fill = "#F5B8B5") +
geom_line(aes(y = roll7), col = "darkred", size = 1) +
labs(x = "Date", y = "Daily New Cases",
title = paste("New Reported Cases by Day in", state.of.interest)) +
theme(plot.background = element_rect(fill = "white"),
panel.background = element_rect(fill = "white"),
plot.title = element_text(size = 14, face = 'bold')) +
theme(aspect.ratio = .5)
`summarise()` ungrouping output (override with `.groups` argument)

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